A VLSI neuroprocessor for image restoration using analog computing-based systolic architecture |
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Authors: | Ji-chien Lee Bing J Sheu and Rama Chellappa |
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Affiliation: | (1) Department of Electrical Engineering, Signal and Image Processing Institute and Center for Neural Engineering, University of Southern California, 90089-0271 Los Angeles, CA |
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Abstract: | An analog computing-based systolic architecture which employs multiple neuroprocessors for high-speed early vision processing is presented. For a two-dimensional image, parallel processing is performed in the row direction and pipelined processing is performed in the column direction. The mixed analog/digital design approach is suitable for implementation of electronic neural systems. Local data computation is executed by analog circuitry to achieve full parallelism and to minimize power dissipation. Inter-processor communication is carried out in the digital format to maintain strong signal strength across the chip boundary and to achieve direct scalability in neural network size. For demonstration purposes, a compact and efficient VLSI neural chip that includes multiple neuroprocessors for high-speed digital image restoration is designed. Measured results of the programmable synapse, and statistical distribution of measured synapse conductances are presented. Based on these results, system-level analyses at 8-bit resolution are conducted. A 8.0×6.0-mm
2 chip from a 1.2-µm CMOS technology can accommodate 5 neuroprocessors and the speed-up factor over the Sun-4/75 SPARC workstation is around 450. This chip achieves 18 Giga connections per second.This research was partially supported by DARPA under Contract MDA 972-90-C-0037 and by TRW Inc., Samsung Electronics Co., Ltd., and NKK Corp. |
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